Background
Our goal is to identify the brain regions most relevant to mental illness using neuroimaging. State of the art machine learning methods commonly suffer from repeatability difficulties in this application, particularly when using large and heterogeneous populations for samples.
New Method
We revisit both dimensionality reduction and sparse modeling, and recast them in a common optimization-based framework. This allows us to combine the benefits of both types of methods in an approach which we call unambiguous components. We use this to estimate the image component with a constrained variability, which is best correlated with the unknown disease mechanism.
Results
We apply the method to the estimation of neuroimaging biomarkers for schizophrenia, using task fMRI data from a large multi-site study. The proposed approach yields an improvement in both robustness of the estimate and classification accuracy.
Comparison with Existing Methods
We find that unambiguous components incorporate roughly two thirds of the same brain regions as sparsity-based methods LASSO and elastic net, while roughly one third of the selected regions differ. Further, unambiguous components achieve superior classification accuracy in differentiating cases from controls.
Conclusions
Unambiguous components provide a robust way to estimate important regions of imaging data.
We consider computational imaging problems where we have an insufficient number of measurements to uniquely reconstruct the object, resulting in an ill-posed inverse problem. Rather than deal with this via the usual regularization approach, which presumes additional information which may be incorrect, we seek bounds on the pixel values of the reconstructed image. Formulating the inverse problem as an optimization problem, we find conditions for which a system's measurements can produce a bounded result for both the linear case and the non-negative case (e.g., intensity imaging). We also consider the problem of selecting measurements to yield the most bounded results. Finally we simulate examples of the application of bounded estimation to different two-dimensional multiview systems.
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